Currently, modern organizations store their data under the form of relational databases which grow faster than hardware capacities. However, ex- tracting information from such databases has become crucial. In this work we propose HTILDE-RT, an algorithm to learn relational regression trees efficiently from huge databases. It is based on the ILP system TILDE and the propositi- onal system VFDT learner. The algorithm uses Hoeffding bound to scale up the learning process. We compared HTILDE-RT with TILDE-RT in two large datasets, each with two million examples, yielding more than three times faster learning times with no statistically significant difference in Pearson correlation coefficient.